DBMS > Axibase vs. Microsoft Access vs. Snowflake vs. Solr vs. Teradata Aster

System Properties Comparison Axibase vs. Microsoft Access vs. Snowflake vs. Solr vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

axibase.com/docs/atsd/finance

www.microsoft.com/en-us/microsoft-365/access

www.snowflake.com

solr.apache.org

Technical documentation

developer.microsoft.com/en-us/access

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Axibase Corporation

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Teradata

Initial release

2013

1992

2014

2006

2005

Current release

15585

1902 (16.0.11328.20222), March 2019

9.6.0, April 2024

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Bundled with Microsoft Office

commercial

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

Server operating systems

Linux

Windows

Not a real database server, but making use of DLLs

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

supports customizable data types and automatic typing

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

in Aster File Store

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

user defined functions

Java plugins

R packages

Triggers

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

but no files for transaction logging

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

a simple user-level security was built in till version Access 2003

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

fine grained access rights according to SQL-standard

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